Abstract：Intelligent satellite technology requires more and more data mining operations for satellite time series data. Usually, satellite data amount is very big that needs a lot of computation, so it will take a very long time to complete the computation in serial program. The satellite anomaly process multi-features analysis procedure is such a typical representation, which performs many common data mining operations, including windows segmentation, computation of vector similarity, feature extraction, Fourier transformation, and cluster-ing. The paper discusses several speed-up and parallel optimization strategies for a time series data mining procedure on a typical heterogeneous computing node with multi-cores CPUs and GPUs, including vector optimization, multi-process parallelization, and GPU computation. We test and compare these optimization strategies in different usage conditions. The experiment results show that the combined use of them can achieve obvious efficiency improvement for different task.